Vttmsi Molecular Sciences Institute

What it reported doing in 2012

Significant activitiesresearch at vtt/msi molecular sciences institute (msi) enables the quantitative prediction of the behaviors of biological systems. To this end, msi emphasizes the creation of tools and techniques for generating biological information, the development of computational and analytical frameworks for integrating this data, and the design and engineering of biological systems. The goal of institute scientists is to significantly advance our understanding of how living systems function and evolve. One way that institute scientists disseminate findings from their research activities is through peer-reviewed publications.below we present select recent publications from the institute: select publicationszdraljevic s, wagner d, cheng k, ruohonen l, jntti j, penttil m, resnekov o, pesce cg. (2013)single cell measurements of enzyme level as a predictive tool for cellular fates during organic acidproduction. Appl environ microbiol. 2013 sep 13. [epub ahead of print] pmid:24038690pincus, d., ryan, c., smith, r.d., brent, r., and resnekov, o. (2013) assigning quatitative function topost-translational modifications reveals multiple sites of phosphorylation that tune yeast pheromonesignaling output. Published 12 mar 2013 | plos one 10.1371/journal.pone.0056544flanigon j, kamali-moghaddam m, burbulis i, annink c, steffen m, oeth p, brent r, van den boom d, landegren u and cantor c. (2012) multiplex protein detection with dna readout via mass spectrometry.n biotechnol. Nov 28. Doi: pii: s1871-6784(12)00860-6. 10.1016/j.nbt.2012.11.003. [epub ahead of print].bush a, chernomoretz a, yu r, gordon a. And colman-lerner a. (2012) using cell-id 1.4 with r formicroscope-based cytometry. Curr protoc mol biol. Oct; chapter 14: unit 14.18. Doi:10.1002/0471142727.mb1418s100.2).denby, c.m., im, j.h., yu, r.c., pesce, g. And brem, r.b. (2012) negative feedback confers mutationalrobustness in yeast transcription factor regulation. Proc. Natl. Acad. Sci. USA. Mar 6: 109(10):3874-3878.thomson tm, benjamin kr, bush a, love t, pincus d, resnekov o, yu rc, gordon a, colman-lernera, endy d and brent r. (2011) scaffold number in yeast signaling system sets tradeoff between systemoutput and dynamic range. Proc. Natl. Acad. Sci. USA. Dec 13;108(50):20265-70. Epub 2011 nov 23.pincus d, benjamin k, burbulis i, tsong ae and resnekov o. (2010) reagents for investigating mapksignalling in model yeast species. Yeast. 2010 jul;27(7):423-30.andrews ss, addy nj, brent r and arkin ap. (2010) detailed simulations of cell biology with smoldyn2.1. Plos comput biol. Mar 12;6(3): e1000705.andrews ss. (2009) accurate particle-based simulation of adsorption, desorption and partial transmission.phys biol. 2009 nov 12;6(4):046015.yu, r.c., pesce, c.g., colman-lerner, a., lok, l., pincus, d., serra, e., holl, m., benjamin, k., gordon, a., and brent , r. (2008) feedback that improves information transmission in yeast signalling. Nature 456:755-761.yu rc, resnekov o, abola ap, andrews ss, benjamin kr, bruck j, burbulis ie, colman-lerner a, endyd, gordon a, holl m, lok l, pesce cg, serra e, smith rd, thomson tm, tsong ae and brent r. (2008)the alpha project: a model system for systems biology research. Iet syst biol. 2008 sep;2(5):222-33.review.burbulis, i., yamaguchi, k., yu, r., resnekov o., and brent, r. (2007) quantifying small numbers ofmolecules with a "near-universal" protein-dna chimera. Nat methods, 12:1011-3.gordon a, colman-lerner a, chin te, benjamin kr, yu, r.c., brent r: (2007) single-cell quantificationof molecules and rates using open-source microscope-based cytometry. Nat methods.burbulis i, yamaguchi k, gordon a, carlson r, brent r: (2005) using protein-dna chimeras to detectand count small numbers of molecules. Nat methods, 2:31-37.fy13 auditcolman-lerner a, gordon a, serra e, chin t, resnekov o, endy d, pesce cg, brent r (2005) regulatedcell-to-cell variation in a cell-fate decision system. Nature, 437:699-706.lok l, brent r: (2005) automatic generat

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Transparency & Controls

• Consistency checks: 100%; rating input score 100; 60th percentile among comparable filings
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• Major donor itemization: Yes; rating input score 100
• Related-party transparency: Limited

Accountability

• Independent voting board: 33.3%; rating input score 33; 9th percentile among comparable filings
• Governance controls: 8 of 8 controls in place; rating input score 100
  • Conflict-of-interest policy: Pass
  • Whistleblower policy: Pass
  • Document-retention policy: Pass
  • CEO compensation review process: NotApplicable
  • Other executive compensation review process: Pass
  • Form 990 shared with the board: Pass
  • Board meeting minutes documented: Pass
  • Audit committee oversight: Pass
  • Independent financial-statement audit: Pass

Sustainability

• Operating margin: 3.7%; rating input score 54; 54th percentile among comparable filings
• Total-asset coverage ratio: 0.15; rating input score 7; 7th percentile among comparable filings
• Liabilities to assets ratio: 1.01; rating input score 8; 8th percentile among comparable filings
• Officer compensation ratio: 14.9%; rating input score 20; 20th percentile among comparable filings

Growth

• Program expense growth: Limited
• Revenue growth: -17.7% per year; rating input score 0; 8th percentile among comparable filings
• Asset growth: -58.9% per year; rating input score 0; 2nd percentile among comparable filings